Modeling the impact of periodic bottlenecks, unidirectional mutation, and observational error in experimental evolution

Antibiotic resistant bacteria are a constant threat in the battle against infectious diseases. One strategy for reducing their effect is to temporarily discontinue the use of certain antibiotics in the hope that in the absence of the antibiotic the resistant strains will be replaced by the sensitive strains. An experiment where this strategy is employed in vitro [5] produces data which showed a slow accumulation of sensitive mutants. Here we propose a mathematical model and statistical analysis to explain this data.The stochastic model elucidates the trend and error structure of the data. It provides a guide for developing future sampling strategies, and provides a framework for long term predictions of the effects of discontinuing specific antibiotics on the dynamics of resistant bacterial populations.This Research is part of the Initiative in Bioinformatics and Evolutionary Studies (IBEST) at the University of Idaho. Funding was provided by NSF EPSCoR EPS-0080935, NSF EPSCoR, EPS-0132626, and NIH NCRR grant NIH NCRR- 20RR016448. Paul Joyce is also funded by NSF DEB-0089756, and NSF DMS-0072198.     

Investigation on the causes of stoichiometric error in genome size estimation using heat experiments: consequences on data interpretation

BACKGROUND AND AIMS: In microdensitometry and flow cytometry, estimation of nuclear DNA content in a sample requires a standard with a known nuclear DNA content. It is assumed that dye accessibility to DNA is the same in the sample and standard nuclei. Stoichiometric error arises when dye accessibility is not proportional between the sample and standard. The aim of the present study was to compare the effects of standardization (external-internal) on nuclear fluorescence of two Coffea species and petunia when temperature increases, and the consequences on genome size estimation. METHODS: Two coffee tree taxa, C. liberica subsp dewevrei (DEW) and C. pseudozanguebarieae (PSE), and Petunia hybrida were grown in a glasshouse in Montpellier, France. Nuclei were extracted by leaf chopping and at least 2 h after nuclei extraction they were stained with propidium iodide for approx. 3 min just before cytometer processing. In the first experiment, effects of heat treatment were observed in mixed (DEW + petunia) and unmixed extracts (petunia and DEW in separate extracts). Nine temperature treatments were carried out (21, 45, 55, 60, 65, 70, 75, 80 and 85 degrees C). In a second experiment, effects of heating on within-species genome size variations were investigated in DEW and PSE. Two temperatures (21 and 70 degrees C) were selected as representative of the maximal range of chromatin decondensation. KEY RESULTS AND CONCLUSIONS: In coffee trees, sample and standard nuclei reacted differently to temperature according to the type of standardization (pseudo-internal vs. external). Cytosolic compounds released in the filtrate would modify chromatin sensitivity to decondensation. Consequently, the 'genome size' estimate depended on the temperature. Similarly, intraspecific variations in genome size changed between estimations at 21 degrees C and 70 degrees C. Consequences are discussed and stoichiometric error detection methods are proposed, along with proposals for minimizing them.     

The structural error-in-equation model to evaluate individual bioequivalence

Individual bioequivalence is assessed using an extension of the classical structural equation model, known as the error-in-equation model. This procedure estimates the relationship between individual means, as well as the variance-covariance parameters, of the bioavailabilities measurement model, by considering individual means related through a straight line with a random term, whereas the classical structural equation considers a deterministic linear relationship. We discuss the implications of this approach in terms of the bioavailabilities measurement model and how to test the overall hypothesis of individual bioequivalence. Both models are compared in a simulation study and a case example is presented.     

Measurement error and confidence intervals for ROC curves

Measurement error in a continuous test variable may bias estimates of the summary properties of receiver operating characteristics (ROC) curves. Typically, unbiased measurement error will reduce the diagnostic potential of a continuous test variable. This paper explores the effects of possibly heterogenous measurement error on estimated ROC curves for binormal test variables. Corrected estimators for specific points on the curve are derived under the assumption of known or estimated measurement variances for individual test results. These estimators and associated confidence intervals do not depend on normal assumptions for the distribution of the measurement error and are shown to be approximately unbiased for moderate size samples in a simulation study. An application from a study of emerging imaging modalities in breast cancer is used to demonstrate the new techniques.     

A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study

In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.     

Estimating pollen flow using SSR markers and paternity exclusion: accounting for mistyping

Highly informative genetic markers, such as simple sequence repeats (SSRs), can be used to directly measure pollen flow by parentage analysis. However, mistyping (i.e. false inference of genotypes caused by the occurrence of null alleles, mutations, and detection errors) can lead to substantial biases in the estimates obtained. Using computer simulations, we evaluated a direct method for estimating pollen immigration using SSR markers and a paternity exclusion approach. This method accounts for mistyping and does not rely on assumptions about the distribution of male reproductive success. If ignored, even minor rates of mistyping (1.5%) resulted in overestimating pollen immigration by up to 150%. When we required at least two mismatching loci before excluding candidate fathers from paternity, the resulting pollen immigration estimates had small biases for rates of mistyping up to 4.5%. Requiring at least three mismatches for exclusion was needed to minimize the upward biases of pollen immigration caused by rates of mistyping up to 10.5%. The minimum number of highly variable SSR loci needed to minimize cryptic gene flow and obtain reliable estimates of pollen immigration varied from five to seven for a sampling scheme applicable to most conifers (i.e. when paternal haplotypes can be unambiguously determined). Between five and nine highly variable SSR loci were needed for a more general sampling scheme that is applicable to all diploid seed plants. With moderately variable SSR markers, consistently accurate estimates of pollen immigration could be obtained only for rates of mistyping up to 4.5%. We developed the POLLEN FLOW (PFL) computer program which can be used to obtain unbiased and precise estimates of pollen immigration under a wide range of conditions, including population sizes as large as 600 parents and mistyping rates as high as 10.5%.     

Unbiased estimation of relative reproductive success of different groups: evaluation and correction of bias caused by parentage assignment errors

Parentage assignment is widely applied to studies on mating systems, population dynamics and natural selection. However, little is known about the consequence of assignment errors, especially when some parents are not sampled. We investigated the effects of two types of error in parentage assignment, failing to assign a true parent (type A) and assigning an untrue parent (type B), on an estimate of the relative reproductive success (RRS) of two groups of parents. Employing a mathematical approach, we found that (i) when all parents are sampled, minimizing either type A or type B error insures the minimum bias on RRS, and (ii) when a large number of parents is not sampled, type B error substantially biases the estimated RRS towards one. Interestingly, however, (iii) when all parents were sampled and both error rates were moderately high, type A error biased the estimated RRS even more than type B error. We propose new methods to obtain an unbiased estimate of RRS and the number of offspring whose parents are not sampled (zW(z)), by correcting the error effects. Applying them to genotypic data from steelhead trout (Oncorhynchus mykiss), we illustrated how to estimate and control the assignment errors. In the data, we observed up to a 30% assignment error and a strong trade-off between the two types of error, depending on the stringency of the assignment decision criterion. We show that our methods can efficiently estimate an unbiased RRS and zW(z) regardless of assignment method, and how to maximize the statistical power to detect a difference in reproductive success between groups.     

The potential costs of accounting for genotypic errors in molecular parentage analyses

Genotypic errors, whether due to mutation or laboratory error, can cause the genotypes of parents and their offspring to appear inconsistent with Mendelian inheritance. As a result, molecular parentage analyses are expected to benefit when allowances are made for the presence of genotypic errors. However, a cost of allowing for genotypic errors might also be expected under some analytical conditions, primarily because parentage analyses that assume nonzero genotypic error rates can neither assign nor exclude parentage with certainty. The goal of this work was therefore to determine whether or not such costs might be important under conditions relevant to parentage analyses, particularly in natural populations. Simulation results indicate that the costs may often outweigh the benefits of accounting for nonzero error rates, except in situations where data are available for many marker loci. Consequently, the most powerful approach to handling genotypic errors in parentage analyses might be to apply likelihood equations with error rates set to values substantially lower than the rates at which genotypic errors occur. When applying molecular parentage analyses to natural populations, we advocate an increased consideration of optimal strategies for handling genotypic errors. Currently available software packages contain procedures that can be used for this purpose.     

Crystal structure of C-terminal desundecapeptide nitrite reductase from Achromobacter cycloclastes

Monoclinic crystal structure of C-terminal desundecapeptide nitrite reductase (NiRc-11) from Achromobacter cycloclastes was determined at 2.6A. NiRc-11 exists as a loose trimer in the crystal. Deletion of 11 residues eliminates all intersubunit hydrogen bonds mediated by the C-terminal tail. The rigid irregular coil 105-112, which constitutes part of the sidewall of the active site pocket, undergoes conformational changes and becomes highly flexible in NiRc-11. Correspondingly, the linker segments between the two copper sites 95-100 and 135-136 are partly relaxed in conformation, which leads to disrupted active site microenvironments responsible for the activity loss and spectral change of NiRc-11. Comparison with the native structure revealed a bulky residue Met331 fastened by hydrogen bonding, which may play a direct role in keeping the right copper site geometry by protruding its side chain against the irregular coil 105-112. Sequence alignment showed that the bulky residue is conserved at position 331, indicating an equal importance of C-terminal segment in other copper-containing nitrite reductases.     

Mitochondrial targeting signals and mature peptides of 3-methylcrotonyl-CoA carboxylase

Inherited deficiency of 3-methylcrotonyl-CoA carboxylase (MCC), an enzyme of leucine degradation, is an organic acidemia detectable by expanded newborn screening with a variable phenotype that ranges from asymptomatic to death in infancy. Here, we show that the two subunits of the enzyme (MCCalpha; MCCbeta) are imported into the mitochondrial matrix by the classical pathway involving cleavable amino-terminal targeting presequences. We identified the cleavage sites (Tyr41/Thr42 and Ala22/Tyr23 for MCCalpha and MCCbeta, respectively) of the targeting signals and the amino-termini of the mature polypeptides of MCC and propionyl-CoA carboxylase, a mitochondrial paralog. The amino-termini containing 39 (MCCalpha) or 20 amino acids (MCCbeta) were both necessary and sufficient for targeting. Structural requirements for mitochondrial import were defined by site-directed mutagenesis. Our studies provide the prerequisite to understand the impact of specific mutations on the clinical phenotype of MCC deficiency.